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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tong Zhao Nehorai, A. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Illinois Univ., Chicago, IL (Tong Zhao; Nehorai, A.) |
| Abstract | We develop an efficient distributed sequential Bayesian estimation method to localize a diffusive source in wireless sensor networks. Potential applications include security, environmental monitoring, pollution control, and explosives detection. We first derive the physical model of the substance dispersion by solving the diffusion equations under different environmental scenarios. We then integrate the derived dispersion models into the distributed processing technologies, and propose a distributed sequential Bayesian localization technique, in which the state belief is transmitted in the wireless sensor networks and updated using the measurements from the new sensor node. In order to decrease the required communication burden we propose two parameterizable belief approximations: a Gaussian approximation and a new linear combination of polynomial Gaussian approximation. We also apply the idea of information-driven sensor scheduling and select the next sensor node according to certain criterions to reduce the response time and save energy consumption of the sensor network |
| Sponsorship | The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS) |
| File Size | 150266 |
| File Format | |
| ISBN | 142440469X |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2006.1661136 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-05-14 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Wireless sensor networks Gaussian approximation Monitoring Pollution control Explosives Differential equations Dispersion Distributed processing Pollution measurement |
| Content Type | Text |
| Resource Type | Article |
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